






"""simple docstring"""
import argparse
import os
from pathlib import Path

import torch
from bark.generation import _load_model as _bark_load_model
from huggingface_hub import hf_hub_download

from transformers import EncodecConfig, EncodecModel, set_seed
from transformers.models.bark.configuration_bark import (
    BarkCoarseConfig,
    BarkConfig,
    BarkFineConfig,
    BarkSemanticConfig,
)
from transformers.models.bark.generation_configuration_bark import (
    BarkCoarseGenerationConfig,
    BarkFineGenerationConfig,
    BarkGenerationConfig,
    BarkSemanticGenerationConfig,
)
from transformers.models.bark.modeling_bark import BarkCoarseModel, BarkFineModel, BarkModel, BarkSemanticModel
from transformers.utils import logging


logging.set_verbosity_info()
snake_case : List[Any]     		=		logging.get_logger(__name__)

set_seed(7_7_0)


snake_case : Optional[Any]     		=		{
    """c_attn""": """att_proj""",
    """c_proj""": """out_proj""",
    """c_fc""": """in_proj""",
    """transformer.""": """""",
    """h.""": """layers.""",
    """ln_1""": """layernorm_1""",
    """ln_2""": """layernorm_2""",
    """ln_f""": """layernorm_final""",
    """wpe""": """position_embeds_layer""",
    """wte""": """input_embeds_layer""",
}


snake_case : Dict     		=		{
    """text_small""": {
        """repo_id""": """suno/bark""",
        """file_name""": """text.pt""",
    },
    """coarse_small""": {
        """repo_id""": """suno/bark""",
        """file_name""": """coarse.pt""",
    },
    """fine_small""": {
        """repo_id""": """suno/bark""",
        """file_name""": """fine.pt""",
    },
    """text""": {
        """repo_id""": """suno/bark""",
        """file_name""": """text_2.pt""",
    },
    """coarse""": {
        """repo_id""": """suno/bark""",
        """file_name""": """coarse_2.pt""",
    },
    """fine""": {
        """repo_id""": """suno/bark""",
        """file_name""": """fine_2.pt""",
    },
}

snake_case : Any     		=		os.path.dirname(os.path.abspath(__file__))
snake_case : List[Any]     		=		os.path.join(os.path.expanduser("""~"""), """.cache""")
snake_case : List[Any]     		=		os.path.join(os.getenv("""XDG_CACHE_HOME""", default_cache_dir), """suno""", """bark_v0""")


def 							A						(				__snake_case: List[str] ,     __snake_case: Dict=False  )    ->		List[str]:

       """simple docstring"""





       __magic_name__      				=    model_type
       if use_small:
              key += "_small"
       return os.path.join(__snake_case ,     REMOTE_MODEL_PATHS[key]['file_name']  )


def 							A						(				__snake_case: int ,     __snake_case: List[Any]  )    ->		Optional[int]:

       """simple docstring"""





       os.makedirs(__snake_case ,     exist_ok=__snake_case  )
       hf_hub_download(repo_id=__snake_case ,     filename=__snake_case ,     local_dir=__snake_case  )


def 							A						(				__snake_case: List[str] ,     __snake_case: Dict ,     __snake_case: List[Any]=False ,     __snake_case: int="text"  )    ->		List[Any]:

       """simple docstring"""





       if model_type == "text":
              __magic_name__      				=    BarkSemanticModel
              __magic_name__      				=    BarkSemanticConfig
              __magic_name__      				=    BarkSemanticGenerationConfig
       elif model_type == "coarse":
              __magic_name__      				=    BarkCoarseModel
              __magic_name__      				=    BarkCoarseConfig
              __magic_name__      				=    BarkCoarseGenerationConfig
       elif model_type == "fine":
              __magic_name__      				=    BarkFineModel
              __magic_name__      				=    BarkFineConfig
              __magic_name__      				=    BarkFineGenerationConfig
       else:
              raise NotImplementedError()
       __magic_name__      				=    F"""{model_type}_small""" if use_small else model_type
       __magic_name__      				=    REMOTE_MODEL_PATHS[model_key]
       if not os.path.exists(__snake_case  ):
              logger.info(F"""{model_type} model not found, downloading into `{CACHE_DIR}`."""  )
              _download(model_info['repo_id'] ,     model_info['file_name']  )
       __magic_name__      				=    torch.load(__snake_case ,     map_location=__snake_case  )
       # this is a hack
       __magic_name__      				=    checkpoint['model_args']
       if "input_vocab_size" not in model_args:
              __magic_name__      				=    model_args['vocab_size']
              __magic_name__      				=    model_args['vocab_size']
              del model_args["vocab_size"]

       # convert Bark model arguments to HF Bark model arguments
       __magic_name__      				=    model_args.pop('n_head'  )
       __magic_name__      				=    model_args.pop('n_embd'  )
       __magic_name__      				=    model_args.pop('n_layer'  )

       __magic_name__      				=    ConfigClass(**checkpoint['model_args']  )
       __magic_name__      				=    ModelClass(config=__snake_case  )
       __magic_name__      				=    GenerationConfigClass()

       __magic_name__      				=    model_generation_config
       __magic_name__      				=    checkpoint['model']
       # fixup checkpoint
       __magic_name__      				=    '_orig_mod.'
       for k, v in list(state_dict.items()  ):
              if k.startswith(__snake_case  ):
                     # replace part of the key with corresponding layer name in HF implementation
                     __magic_name__      				=    k[len(__snake_case  ) :]
                     for old_layer_name in new_layer_name_dict:
                            __magic_name__      				=    new_k.replace(__snake_case ,     new_layer_name_dict[old_layer_name]  )

                     __magic_name__      				=    state_dict.pop(__snake_case  )

       __magic_name__      				=    set(state_dict.keys()  ) - set(model.state_dict().keys()  )
       __magic_name__      				=    {k for k in extra_keys if not k.endswith('.attn.bias'  )}
       __magic_name__      				=    set(model.state_dict().keys()  ) - set(state_dict.keys()  )
       __magic_name__      				=    {k for k in missing_keys if not k.endswith('.attn.bias'  )}
       if len(__snake_case  ) != 0:
              raise ValueError(F"""extra keys found: {extra_keys}"""  )
       if len(__snake_case  ) != 0:
              raise ValueError(F"""missing keys: {missing_keys}"""  )
       model.load_state_dict(__snake_case ,     strict=__snake_case  )
       __magic_name__      				=    model.num_parameters(exclude_embeddings=__snake_case  )
       __magic_name__      				=    checkpoint['best_val_loss'].item()
       logger.info(F"""model loaded: {round(n_params/1E6 ,     1  )}M params, {round(__snake_case ,     3  )} loss"""  )
       model.eval()
       model.to(__snake_case  )
       del checkpoint, state_dict

       return model


def 							A						(				__snake_case: Dict ,     __snake_case: Dict=False ,     __snake_case: int="text"  )    ->		int:

       """simple docstring"""





       if model_type not in ("text", "coarse", "fine"):
              raise NotImplementedError()

       __magic_name__      				=    'cpu'  # do conversion on cpu

       __magic_name__      				=    _get_ckpt_path(__snake_case ,     use_small=__snake_case  )
       __magic_name__      				=    _load_model(__snake_case ,     __snake_case ,     model_type=__snake_case ,     use_small=__snake_case  )

       # load bark initial model
       __magic_name__      				=    _bark_load_model(__snake_case ,     'cpu' ,     model_type=__snake_case ,     use_small=__snake_case  )

       if model_type == "text":
              __magic_name__      				=    bark_model['model']

       if model.num_parameters(exclude_embeddings=__snake_case  ) != bark_model.get_num_params():
              raise ValueError('initial and new models don\'t have the same number of parameters'  )

       # check if same output as the bark model
       __magic_name__      				=    5
       __magic_name__      				=    1_0

       if model_type in ["text", "coarse"]:
              __magic_name__      				=    torch.randint(2_5_6 ,     (batch_size, sequence_length) ,     dtype=torch.int  )
              __magic_name__      				=    bark_model(__snake_case  )[0]

              __magic_name__      				=    model(__snake_case  )

              # take last logits
              __magic_name__      				=    output_new_model_total.logits[:, [-1], :]

       else:
              __magic_name__      				=    3
              __magic_name__      				=    8
              __magic_name__      				=    torch.randint(2_5_6 ,     (batch_size, sequence_length, n_codes_total) ,     dtype=torch.int  )

              __magic_name__      				=    model(__snake_case ,     __snake_case  )
              __magic_name__      				=    bark_model(__snake_case ,     __snake_case  )

              __magic_name__      				=    output_new_model_total.logits

       # output difference should come from the difference of self-attention implementation design
       if output_new_model.shape != output_old_model.shape:
              raise ValueError('initial and new outputs don\'t have the same shape'  )
       if (output_new_model - output_old_model).abs().max().item() > 1E-3:
              raise ValueError('initial and new outputs are not equal'  )

       Path(__snake_case  ).mkdir(exist_ok=__snake_case  )
       model.save_pretrained(__snake_case  )


def 							A						(				__snake_case: Any ,     __snake_case: Optional[Any] ,     __snake_case: Optional[Any] ,     __snake_case: List[str] ,     __snake_case: List[str] ,     __snake_case: int ,     )    ->		Optional[int]:

       """simple docstring"""





       __magic_name__      				=    os.path.join(__snake_case ,     __snake_case  )

       __magic_name__      				=    BarkSemanticConfig.from_pretrained(os.path.join(__snake_case ,     'config.json'  )  )
       __magic_name__      				=    BarkCoarseConfig.from_pretrained(os.path.join(__snake_case ,     'config.json'  )  )
       __magic_name__      				=    BarkFineConfig.from_pretrained(os.path.join(__snake_case ,     'config.json'  )  )
       __magic_name__      				=    EncodecConfig.from_pretrained('facebook/encodec_24khz'  )

       __magic_name__      				=    BarkSemanticModel.from_pretrained(__snake_case  )
       __magic_name__      				=    BarkCoarseModel.from_pretrained(__snake_case  )
       __magic_name__      				=    BarkFineModel.from_pretrained(__snake_case  )
       __magic_name__      				=    EncodecModel.from_pretrained('facebook/encodec_24khz'  )

       __magic_name__      				=    BarkConfig.from_sub_model_configs(
           __snake_case ,     __snake_case ,     __snake_case ,     __snake_case  )

       __magic_name__      				=    BarkGenerationConfig.from_sub_model_configs(
           semantic.generation_config ,     coarseAcoustic.generation_config ,     fineAcoustic.generation_config  )

       __magic_name__      				=    BarkModel(__snake_case  )

       __magic_name__      				=    semantic
       __magic_name__      				=    coarseAcoustic
       __magic_name__      				=    fineAcoustic
       __magic_name__      				=    codec

       __magic_name__      				=    bark_generation_config

       Path(__snake_case  ).mkdir(exist_ok=__snake_case  )
       bark.save_pretrained(__snake_case ,     repo_id=__snake_case ,     push_to_hub=__snake_case  )


if __name__ == "__main__":
 snake_case : Any     		=		argparse.ArgumentParser()
 # Required parameters

 parser.add_argument("""model_type""", type=str, help="""text, coarse or fine.""")
 parser.add_argument("""pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
 parser.add_argument("""--is_small""", action="""store_true""", help="""convert the small version instead of the large.""")

 snake_case : str     		=		parser.parse_args()

 load_model(args.pytorch_dump_folder_path, model_type=args.model_type, use_small=args.is_small)







"""simple docstring"""
from ..utils import DummyObject, requires_backends



class        UpperCamelCase__ (				metaclass=a_):







    """simple docstring"""
    __UpperCAmelCase           = ["""flax""", """transformers"""]







    def __init__(		self :	List[Any]				,    *UpperCamelCase_ :	str				,    **UpperCamelCase_ :	Any ):





           '''simple docstring'''

           requires_backends(self				,    ['flax', 'transformers'] )







    @classmethod
    def 				a__			(		cls :	Optional[int]				,    *UpperCamelCase_ :	Tuple				,    **UpperCamelCase_ :	List[Any] ):





           '''simple docstring'''

           requires_backends(cls				,    ['flax', 'transformers'] )







    @classmethod
    def 				a__			(		cls :	str				,    *UpperCamelCase_ :	Optional[Any]				,    **UpperCamelCase_ :	Dict ):





           '''simple docstring'''

           requires_backends(cls				,    ['flax', 'transformers'] )



class        UpperCamelCase__ (				metaclass=a_):







    """simple docstring"""
    __UpperCAmelCase           = ["""flax""", """transformers"""]







    def __init__(		self :	List[Any]				,    *UpperCamelCase_ :	str				,    **UpperCamelCase_ :	str ):





           '''simple docstring'''

           requires_backends(self				,    ['flax', 'transformers'] )







    @classmethod
    def 				a__			(		cls :	Optional[int]				,    *UpperCamelCase_ :	Optional[int]				,    **UpperCamelCase_ :	List[str] ):





           '''simple docstring'''

           requires_backends(cls				,    ['flax', 'transformers'] )







    @classmethod
    def 				a__			(		cls :	Tuple				,    *UpperCamelCase_ :	List[str]				,    **UpperCamelCase_ :	Optional[Any] ):





           '''simple docstring'''

           requires_backends(cls				,    ['flax', 'transformers'] )



class        UpperCamelCase__ (				metaclass=a_):







    """simple docstring"""
    __UpperCAmelCase           = ["""flax""", """transformers"""]







    def __init__(		self :	Optional[int]				,    *UpperCamelCase_ :	List[Any]				,    **UpperCamelCase_ :	List[str] ):





           '''simple docstring'''

           requires_backends(self				,    ['flax', 'transformers'] )







    @classmethod
    def 				a__			(		cls :	str				,    *UpperCamelCase_ :	List[str]				,    **UpperCamelCase_ :	Tuple ):





           '''simple docstring'''

           requires_backends(cls				,    ['flax', 'transformers'] )







    @classmethod
    def 				a__			(		cls :	Tuple				,    *UpperCamelCase_ :	Any				,    **UpperCamelCase_ :	List[str] ):





           '''simple docstring'''

           requires_backends(cls				,    ['flax', 'transformers'] )



class        UpperCamelCase__ (				metaclass=a_):







    """simple docstring"""
    __UpperCAmelCase           = ["""flax""", """transformers"""]







    def __init__(		self :	Optional[Any]				,    *UpperCamelCase_ :	Optional[Any]				,    **UpperCamelCase_ :	Dict ):





           '''simple docstring'''

           requires_backends(self				,    ['flax', 'transformers'] )







    @classmethod
    def 				a__			(		cls :	str				,    *UpperCamelCase_ :	Dict				,    **UpperCamelCase_ :	List[str] ):





           '''simple docstring'''

           requires_backends(cls				,    ['flax', 'transformers'] )







    @classmethod
    def 				a__			(		cls :	str				,    *UpperCamelCase_ :	Optional[Any]				,    **UpperCamelCase_ :	Union[str, Any] ):





           '''simple docstring'''

           requires_backends(cls				,    ['flax', 'transformers'] )